AI agents can browse your CRM, read your database, and scan your inbox without causing damage. The real risk starts when they write - sending an email to a client, issuing a refund, or updating a production database. Those actions can't be undone with a quick rollback.
Gate is a new open-source tool that addresses this problem by sitting between your AI agent and your production systems. The concept is straightforward: instead of letting an agent execute write operations directly, the agent submits an "intent" describing what it wants to do. Gate then evaluates that intent against a set of YAML policy rules and either approves, blocks, or flags it for human review.
The key design choice here is that Gate uses no LLM during the evaluation step. Policy evaluation is fully deterministic - the same input always produces the same output. That matters because you don't want the safety layer itself introducing unpredictable behavior. The flow works like this: the agent sends a POST request to Gate's intent endpoint, the YAML policy gets evaluated, and if approved, Gate issues a signed execution order that the downstream system can verify.
This is the kind of infrastructure that becomes important as companies move AI agents from "read-only assistants" into workflows where they actually take actions. Right now, most teams handle this with ad-hoc approval queues or hardcoded guardrails baked into the agent code. A dedicated policy layer that separates "what the agent wants to do" from "what the agent is allowed to do" is a cleaner pattern.
Gate is early-stage and targeted squarely at developers building agent pipelines. It won't matter to anyone using ChatGPT for writing help, but for teams deploying agents that touch CRMs, payment systems, or customer communications, the problem it solves is real and growing.